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Machine Learning Foundations: A Case Study Approach(으)로 돌아가기

워싱턴 대학교의 Machine Learning Foundations: A Case Study Approach 학습자 리뷰 및 피드백

4.6
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10,901개의 평가
2,616개의 리뷰

강좌 소개

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python....

최상위 리뷰

BL

Oct 17, 2016

Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much

SZ

Dec 20, 2016

Great course!\n\nEmily and Carlos teach this class in a very interest way. They try to let student understand machine learning by some case study. That worked well on me. I like this course very much.

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 2,531개 리뷰 중 2051~2075

교육 기관: Bruno C

Oct 13, 2016

I enjoyed the course.

I wish it had more machine data driven models to to address more industrial type problems, for instance Predictive maintenance.

교육 기관: Marta C G

Dec 11, 2019

The course was OK, but I would introduce more about scikit-learn rather than a library that can only be installed in MacOS or Linux in an easier way

교육 기관: Ha T N

Mar 05, 2018

That's a good course overall, but the implementation is too much depend on graphlab. It would be nicer if the instructors switch to use scikit-learn

교육 기관: Giorgi G

Dec 24, 2015

It was more about learning DATO then any insight. However I understand that this course is good motivator for beginners, but was very boring for me.

교육 기관: Radu C

Sep 01, 2018

A nice intro to ML foundations. Will enroll into the follow up courses from this specialization.

Using python2 and graphlab was a bit of a turn off.

교육 기관: FLORES

Dec 15, 2016

A good introduction to ML applications, but not as detailed and thorough as I expected. I'm looking forward to the following of the specialization.

교육 기관: Narasimha P

Feb 21, 2016

The course is an excellent starter to understand the foundations of Machine Learning. The Assignments could a bit more involved and have more rigor

교육 기관: Shalsangz

Dec 25, 2015

Thank you Emily & Carlos! I really enjoyed the course. It was very well taught with clear explanations. Looking forward to the rest of the courses!

교육 기관: Yousef Z

Dec 05, 2017

It is a very good course to start in machine learning. I liked it. But I think it need more details in Graphlab and how it is really works inside.

교육 기관: Luis C

Sep 26, 2016

Actually the course goes great, just a cuople onf the intro video are useless, and in fact distract the real value of the course in its first week

교육 기관: Shridhar A H

Jul 29, 2020

This is the bets course to learn ML as it teaches you through project base learning which most of the courses don't do. It's really a nice course

교육 기관: Alon H

Feb 12, 2016

Deep learning session is a bit unclear in that it doesn't give a pratical example of how a simple Nueral network can be mapped to a deep-feature.

교육 기관: Fred M B

Apr 08, 2016

The ML Foundations course provides a good overview of the other courses in the specialization. It also provides a good introduction to GraphLab.

교육 기관: Rishabh s

Jul 14, 2020

The course is great. The only problem was Graphlab and unfortunately i have to convert the code for pandas. Overall the course was informative

교육 기관: Yashodhar P P

Jul 12, 2020

Fantastic way of teaching. Only thing that I felt uncomfortable is that practical videos are old and mentioned graphlab instead of turicreate.

교육 기관: Kingshuk C

Dec 14, 2015

Provides a good overview of the machine learning branches. Makes sense to approach it as a black box and then take deep dives in later series.

교육 기관: Sunil N

Jan 31, 2020

Updated videos in line with Turicreate could be of great help. Thoroughly enjoyed the learning experience though! Thanks to both the tutors!

교육 기관: Andrew V G

Dec 25, 2017

Good introduction. I wish the notebooks had been a little more interactive, and there was more emphasis on applying the methods to new data.

교육 기관: Anshul T

Dec 26, 2015

A good course to show the extent of what is coming up in the specialization, a quick and dirty hands-on approach and pleasant course tutors.

교육 기관: Jiancheng

Dec 06, 2015

Great course design and case study! More detail about algorithm will be better, but maybe you can find them more in the following courses.

교육 기관: TE Z

Dec 21, 2018

A good way to get familiar with the machine learning things. it provides the very basic concepts and way of thinking from ML perspective.

교육 기관: Dina T S

Feb 10, 2016

The overall course was very useful.

However some topics I found difficulty to understand the idea even after repeating the video 3 times.

교육 기관: Lucas M

Nov 11, 2018

I couldn't install Graphlab create on my own environment, so I had to translate almost everything to Pandas/SKLearn.

Great content, still

교육 기관: Yogesh D

Apr 14, 2016

its a good course to get hands on development with machine learning algorithms. but one disadvantage being its using preparatory library

교육 기관: ZHENG W

Sep 17, 2017

Great intro course to machines learning. For those who perhaps learnt back in university, it can also serve as a great revision course.